Mathias Hauan Arbo
Norwegian University of Science and Technology, Trondheim Kommune, SINTEF
Papers
14
Total Citations
133
H-Index
8
About
Mathias Hauan Arbo is a leading researcher in robotic assembly and manufacturing, whose work bridges the gap between digital design and physical automation. His core research areas include constraint-based robot programming, model-based definition (MBD), and the integration of STEP AP242 standards for industrial robotics. Arbo’s major contribution lies in developing system architectures that translate CAD-level product data—such as geometric dimensioning and tolerancing (GD&T)—directly into sensor-based robot skills for assembly and welding. His most-cited paper, "Leveraging model based definition and STEP AP242 in task specification for robotic assembly" (21 citations), exemplifies this by enabling an unbroken data thread from design to execution. His work on model predictive control for industrial robots (12 citations) further showcases his expertise in advanced motion planning. Notably, Arbo has also contributed open-source tools like "Robot Dynamics with URDF & CasADi" (9 citations), which accelerate symbolic dynamics computation for researchers. With over 100 total citations across his top papers, Arbo’s research is pivotal for Industry 4.0, offering practical pathways to smarter, data-driven factories.
Research Focus
Key Achievements
Top Papers
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- 5Comparison of KVP and RSI for Controlling KUKA Robots Over ROS10 citations · 2020
- 6Robot Dynamics with URDF & CasADi9 citations · 2019
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